Impact of many factors on the stock prices makes the stock prediction a difficult and highly complicated task. In this paper, machine learning techniques have been applied for the stock price prediction in order to overcome such difficulties. In the implemented work, five models have been developed and their performances are compared in predicting the stock market trends.** We evaluate OTAQ PLC prediction models with Modular Neural Network (Financial Sentiment Analysis) and Lasso Regression ^{1,2,3,4} and conclude that the LON:OTAQ stock is predictable in the short/long term. **

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:OTAQ stock.**

**LON:OTAQ, OTAQ PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Prediction Modeling
- Prediction Modeling
- Buy, Sell and Hold Signals

## LON:OTAQ Target Price Prediction Modeling Methodology

The success of portfolio construction depends primarily on the future performance of stock markets. Recent developments in machine learning have brought significant opportunities to incorporate prediction theory into portfolio selection. However, many studies show that a single prediction model is insufficient to achieve very accurate predictions and affluent returns. In this paper, a novel portfolio construction approach is developed using a hybrid model based on machine learning for stock prediction. We consider OTAQ PLC Stock Decision Process with Lasso Regression where A is the set of discrete actions of LON:OTAQ stock holders, F is the set of discrete states, P : S × F × S → R is the transition probability distribution, R : S × F → R is the reaction function, and γ ∈ [0, 1] is a move factor for expectation.^{1,2,3,4}

F(Lasso Regression)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (Financial Sentiment Analysis)) X S(n):→ (n+4 weeks) $\sum _{i=1}^{n}\left({a}_{i}\right)$

n:Time series to forecast

p:Price signals of LON:OTAQ stock

j:Nash equilibria

k:Dominated move

a:Best response for target price

For further technical information as per how our model work we invite you to visit the article below:

How do AC Investment Research machine learning (predictive) algorithms actually work?

## LON:OTAQ Stock Forecast (Buy or Sell) for (n+4 weeks)

**Sample Set:**Neural Network

**Stock/Index:**LON:OTAQ OTAQ PLC

**Time series to forecast n: 24 Oct 2022**for (n+4 weeks)

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:OTAQ stock.**

**X axis: *Likelihood%** (The higher the percentage value, the more likely the event will occur.)

**Y axis: *Potential Impact%** (The higher the percentage value, the more likely the price will deviate.)

**Z axis (Yellow to Green): *Technical Analysis%**

## Conclusions

OTAQ PLC assigned short-term Ba3 & long-term B2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with Lasso Regression ^{1,2,3,4} and conclude that the LON:OTAQ stock is predictable in the short/long term.**

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:OTAQ stock.**

### Financial State Forecast for LON:OTAQ Stock Options & Futures

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | Ba3 | B2 |

Operational Risk | 38 | 63 |

Market Risk | 53 | 82 |

Technical Analysis | 78 | 32 |

Fundamental Analysis | 88 | 62 |

Risk Unsystematic | 68 | 30 |

### Prediction Confidence Score

## References

- Hornik K, Stinchcombe M, White H. 1989. Multilayer feedforward networks are universal approximators. Neural Netw. 2:359–66
- Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
- Artis, M. J. W. Zhang (1990), "BVAR forecasts for the G-7," International Journal of Forecasting, 6, 349–362.
- Chamberlain G. 2000. Econometrics and decision theory. J. Econom. 95:255–83
- M. Petrik and D. Subramanian. An approximate solution method for large risk-averse Markov decision processes. In Proceedings of the 28th International Conference on Uncertainty in Artificial Intelligence, 2012.
- Thomas P, Brunskill E. 2016. Data-efficient off-policy policy evaluation for reinforcement learning. In Pro- ceedings of the International Conference on Machine Learning, pp. 2139–48. La Jolla, CA: Int. Mach. Learn. Soc.
- Breiman L. 1996. Bagging predictors. Mach. Learn. 24:123–40

## Frequently Asked Questions

Q: What is the prediction methodology for LON:OTAQ stock?A: LON:OTAQ stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Lasso Regression

Q: Is LON:OTAQ stock a buy or sell?

A: The dominant strategy among neural network is to Hold LON:OTAQ Stock.

Q: Is OTAQ PLC stock a good investment?

A: The consensus rating for OTAQ PLC is Hold and assigned short-term Ba3 & long-term B2 forecasted stock rating.

Q: What is the consensus rating of LON:OTAQ stock?

A: The consensus rating for LON:OTAQ is Hold.

Q: What is the prediction period for LON:OTAQ stock?

A: The prediction period for LON:OTAQ is (n+4 weeks)

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